{"id":"W4283584646","doi":"10.11159/ffhmt22.132","title":"Machine Learning Based Statistical Characterization of a Turbulence Dissipation Rate Array: A Revisitation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dissipation; Turbulence; Characterization (materials science); Computer science; Artificial intelligence; Physics; Mechanics; Optics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003562805,0.00009788349,0.0001432619,0.00006694492,0.0001630067,0.00003752831,0.0001972368,0.00002664961,0.002161189],"category_scores_gemma":[0.00008438112,0.00007174996,0.00004251048,0.0001321556,0.00006627516,0.0001493093,0.00000967994,0.0001596261,0.000001838223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008442857,"about_ca_system_score_gemma":0.00002718798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003971113,"about_ca_topic_score_gemma":0.000003372931,"domain_scores_codex":[0.9990348,0.00004728977,0.0002679456,0.0001883431,0.0003606716,0.0001009357],"domain_scores_gemma":[0.9996032,0.0001031413,0.00004999877,0.00003909695,0.0001615223,0.00004311797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001260692,0.0001130251,0.09642474,0.0001378748,0.00005409262,5.642986e-7,0.0008729574,0.02291548,0.8128895,0.05565314,0.00001357437,0.009664358],"study_design_scores_gemma":[0.0004402165,0.0003447538,0.08257309,0.00004253655,0.00002592499,8.541618e-7,0.00009088217,0.9069649,0.004774617,0.004339938,0.0002905371,0.000111755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842248,0.00002113563,0.009576277,0.001692634,0.0001605591,0.0002534667,0.0004667527,0.00001669065,0.00358768],"genre_scores_gemma":[0.998929,0.00004055867,0.0004596314,0.0001668622,0.00002152212,0.000008612446,0.0002869659,0.000003066558,0.00008384223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8840494,"threshold_uncertainty_score":0.998751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464378716843636,"score_gpt":0.2280459043621078,"score_spread":0.2034021171936715,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}